



import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


__lowerCamelCase  :				Tuple					=					logging.getLogger(__name__)





class 			__magic_name__ (				_lowerCAmelCase    ):
		lowercase   :							List[Any]        ='''token-classification'''






		def __init__(    self   :  str					,		UpperCamelCase__   :  Union[str, Any]   )					->       List[str]:
								'''simple docstring'''







								if type(SCREAMING_SNAKE_CASE_   ) == dict:
														UpperCAmelCase								=  Namespace(**SCREAMING_SNAKE_CASE_   )
								UpperCAmelCase								=  import_module("tasks"   )
								try:
														UpperCAmelCase								=  getattr(SCREAMING_SNAKE_CASE_					,		hparams.task_type   )
														UpperCAmelCase								=  token_classification_task_clazz()
								except AttributeError:
														raise ValueError(
														    F'Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
														    F'Available tasks classes are: {TokenClassificationTask.__subclasses__()}'   )
								UpperCAmelCase								=  self.token_classification_task.get_labels(hparams.labels   )
								UpperCAmelCase								=  CrossEntropyLoss().ignore_index
								super().__init__(SCREAMING_SNAKE_CASE_					,		len(self.labels   )					,		self.mode   )






		def SCREAMING_SNAKE_CASE_      (    self   :  Tuple					,		**UpperCamelCase__   :  Tuple   )					->       List[Any]:
								'''simple docstring'''







								return self.model(**SCREAMING_SNAKE_CASE_   )






		def SCREAMING_SNAKE_CASE_      (    self   :  Union[str, Any]					,		UpperCamelCase__   :  Dict					,		UpperCamelCase__   :  int   )					->       Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase								=  {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
								if self.config.model_type != "distilbert":
														UpperCAmelCase								=  (
														    batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
														)  # XLM and RoBERTa don"t use token_type_ids

								UpperCAmelCase								=  self(**SCREAMING_SNAKE_CASE_   )
								UpperCAmelCase								=  outputs[0]
								# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
								return {"loss": loss}






		def SCREAMING_SNAKE_CASE_      (    self   :  Dict   )					->       Dict:
								'''simple docstring'''







								UpperCAmelCase								=  self.hparams
								for mode in ["train", "dev", "test"]:
														UpperCAmelCase								=  self._feature_file(SCREAMING_SNAKE_CASE_   )
														if os.path.exists(SCREAMING_SNAKE_CASE_   ) and not args.overwrite_cache:
																				logger.info("Loading features from cached file %s"					,		SCREAMING_SNAKE_CASE_   )
																				UpperCAmelCase								=  torch.load(SCREAMING_SNAKE_CASE_   )
														else:
																				logger.info("Creating features from dataset file at %s"					,		args.data_dir   )
																				UpperCAmelCase								=  self.token_classification_task.read_examples_from_file(args.data_dir					,		SCREAMING_SNAKE_CASE_   )
																				UpperCAmelCase								=  self.token_classification_task.convert_examples_to_features(
																				    SCREAMING_SNAKE_CASE_					,		self.labels					,		args.max_seq_length					,		self.tokenizer					,		cls_token_at_end=bool(self.config.model_type in ["xlnet"]   )					,		cls_token=self.tokenizer.cls_token					,		cls_token_segment_id=2 if self.config.model_type in ["xlnet"] else 0					,		sep_token=self.tokenizer.sep_token					,		sep_token_extra=SCREAMING_SNAKE_CASE_					,		pad_on_left=bool(self.config.model_type in ["xlnet"]   )					,		pad_token=self.tokenizer.pad_token_id					,		pad_token_segment_id=self.tokenizer.pad_token_type_id					,		pad_token_label_id=self.pad_token_label_id					,		)
																				logger.info("Saving features into cached file %s"					,		SCREAMING_SNAKE_CASE_   )
																				torch.save(SCREAMING_SNAKE_CASE_					,		SCREAMING_SNAKE_CASE_   )






		def SCREAMING_SNAKE_CASE_      (    self   :  Dict					,		UpperCamelCase__   :  Optional[int]					,		UpperCamelCase__   :  Any					,		UpperCamelCase__   :  List[Any] = False   )					->       DataLoader:
								'''simple docstring'''







								UpperCAmelCase								=  self._feature_file(SCREAMING_SNAKE_CASE_   )
								logger.info("Loading features from cached file %s"					,		SCREAMING_SNAKE_CASE_   )
								UpperCAmelCase								=  torch.load(SCREAMING_SNAKE_CASE_   )
								UpperCAmelCase								=  torch.tensor([f.input_ids for f in features]					,		dtype=torch.long   )
								UpperCAmelCase								=  torch.tensor([f.attention_mask for f in features]					,		dtype=torch.long   )
								if features[0].token_type_ids is not None:
														UpperCAmelCase								=  torch.tensor([f.token_type_ids for f in features]					,		dtype=torch.long   )
								else:
														UpperCAmelCase								=  torch.tensor([0 for f in features]					,		dtype=torch.long   )
														# HACK(we will not use this anymore soon)
								UpperCAmelCase								=  torch.tensor([f.label_ids for f in features]					,		dtype=torch.long   )
								return DataLoader(
								    TensorDataset(SCREAMING_SNAKE_CASE_					,		SCREAMING_SNAKE_CASE_					,		SCREAMING_SNAKE_CASE_					,		SCREAMING_SNAKE_CASE_   )					,		batch_size=SCREAMING_SNAKE_CASE_   )






		def SCREAMING_SNAKE_CASE_      (    self   :  Any					,		UpperCamelCase__   :  Optional[int]					,		UpperCamelCase__   :  Optional[Any]   )					->       int:
								'''simple docstring'''







								"""Compute validation""" ""
								UpperCAmelCase								=  {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
								if self.config.model_type != "distilbert":
														UpperCAmelCase								=  (
														    batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
														)  # XLM and RoBERTa don"t use token_type_ids
								UpperCAmelCase								=  self(**SCREAMING_SNAKE_CASE_   )
								UpperCAmelCase								=  outputs[:2]
								UpperCAmelCase								=  logits.detach().cpu().numpy()
								UpperCAmelCase								=  inputs["""labels"""].detach().cpu().numpy()
								return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}






		def SCREAMING_SNAKE_CASE_      (    self   :  str					,		UpperCamelCase__   :  Union[str, Any]   )					->       Union[str, Any]:
								'''simple docstring'''







								UpperCAmelCase								=  torch.stack([x["val_loss"] for x in outputs]   ).mean()
								UpperCAmelCase								=  np.concatenate([x["pred"] for x in outputs]					,		axis=0   )
								UpperCAmelCase								=  np.argmax(SCREAMING_SNAKE_CASE_					,		axis=2   )
								UpperCAmelCase								=  np.concatenate([x["target"] for x in outputs]					,		axis=0   )

								UpperCAmelCase								=  dict(enumerate(self.labels   )   )
								UpperCAmelCase								=  [[] for _ in range(out_label_ids.shape[0]   )]
								UpperCAmelCase								=  [[] for _ in range(out_label_ids.shape[0]   )]

								for i in range(out_label_ids.shape[0]   ):
														for j in range(out_label_ids.shape[1]   ):
																				if out_label_ids[i, j] != self.pad_token_label_id:
																										out_label_list[i].append(label_map[out_label_ids[i][j]]   )
																										preds_list[i].append(label_map[preds[i][j]]   )

								UpperCAmelCase								=  {
								    """val_loss""": val_loss_mean,
								    """accuracy_score""": accuracy_score(SCREAMING_SNAKE_CASE_					,		SCREAMING_SNAKE_CASE_   ),
								    """precision""": precision_score(SCREAMING_SNAKE_CASE_					,		SCREAMING_SNAKE_CASE_   ),
								    """recall""": recall_score(SCREAMING_SNAKE_CASE_					,		SCREAMING_SNAKE_CASE_   ),
								    """f1""": fa_score(SCREAMING_SNAKE_CASE_					,		SCREAMING_SNAKE_CASE_   ),
								}

								UpperCAmelCase								=  dict(results.items()   )
								UpperCAmelCase								=  results
								return ret, preds_list, out_label_list






		def SCREAMING_SNAKE_CASE_      (    self   :  Tuple					,		UpperCamelCase__   :  Optional[int]   )					->       Dict:
								'''simple docstring'''







								UpperCAmelCase								=  self._eval_end(SCREAMING_SNAKE_CASE_   )
								UpperCAmelCase								=  ret["""log"""]
								return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






		def SCREAMING_SNAKE_CASE_      (    self   :  Union[str, Any]					,		UpperCamelCase__   :  int   )					->       Union[str, Any]:
								'''simple docstring'''







								UpperCAmelCase								=  self._eval_end(SCREAMING_SNAKE_CASE_   )

								# Converting to the dict required by pl
								# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
								# pytorch_lightning/trainer/logging.py#L139
								UpperCAmelCase								=  ret["""log"""]
								# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
								return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







		@staticmethod
		def SCREAMING_SNAKE_CASE_      (    UpperCamelCase__   :  Optional[Any]					,		UpperCamelCase__   :  Optional[int]   )					->       Union[str, Any]:
								'''simple docstring'''







								BaseTransformer.add_model_specific_args(SCREAMING_SNAKE_CASE_					,		SCREAMING_SNAKE_CASE_   )
								parser.add_argument(
								    "--task_type"					,		default="NER"					,		type=SCREAMING_SNAKE_CASE_					,		help="Task type to fine tune in training (e.g. NER, POS, etc)"   )
								parser.add_argument(
								    "--max_seq_length"					,		default=1_28					,		type=SCREAMING_SNAKE_CASE_					,		help=(
								        "The maximum total input sequence length after tokenization. Sequences longer "
								        "than this will be truncated, sequences shorter will be padded."
								    )					,		)

								parser.add_argument(
								    "--labels"					,		default=""					,		type=SCREAMING_SNAKE_CASE_					,		help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."					,		)
								parser.add_argument(
								    "--gpus"					,		default=0					,		type=SCREAMING_SNAKE_CASE_					,		help="The number of GPUs allocated for this, it is by default 0 meaning none"					,		)

								parser.add_argument(
								    "--overwrite_cache"					,		action="store_true"					,		help="Overwrite the cached training and evaluation sets"   )

								return parser


if __name__ == "__main__":
		__lowerCamelCase  :				Optional[Any]					=					argparse.ArgumentParser()
		add_generic_args(parser, os.getcwd())
		__lowerCamelCase  :				List[Any]					=					NERTransformer.add_model_specific_args(parser, os.getcwd())
		__lowerCamelCase  :				List[str]					=					parser.parse_args()
		__lowerCamelCase  :				Tuple					=					NERTransformer(args)
		__lowerCamelCase  :				Any					=					generic_train(model, args)

		if args.do_predict:
				# See https://github.com/huggingface/transformers/issues/3159
				# pl use this default format to create a checkpoint:
				# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
				# /pytorch_lightning/callbacks/model_checkpoint.py#L322
				__lowerCamelCase  :				Optional[int]					=					sorted(glob.glob(os.path.join(args.output_dir, "checkpoint-epoch=*.ckpt"), recursive=True))
				__lowerCamelCase  :				str					=					model.load_from_checkpoint(checkpoints[-1])
				trainer.test(model)








def  lowerCAmelCase_	(lowerCAmelCase__:		int  ):





			"""simple docstring"""


			if n == 1 or not isinstance(lowerCAmelCase__		,						lowerCAmelCase__  ):
						return 0
			elif n == 2:
						return 1
			else:
						UpperCAmelCase_:		Optional[int]							      =     [0, 1]
						for i in range(2		,						n + 1  ):
									sequence.append(sequence[i - 1] + sequence[i - 2]  )

						return sequence[n]







def  lowerCAmelCase_	(lowerCAmelCase__:		int  ):





			"""simple docstring"""


			UpperCAmelCase_:		str							      =     0
			UpperCAmelCase_:		str							      =     2

			while digits < n:
						index += 1
						UpperCAmelCase_:		Union[str, Any]							      =     len(str(fibonacci(lowerCAmelCase__  )  )  )

			return index







def  lowerCAmelCase_	(lowerCAmelCase__:		int = 1_0_0_0  ):





			"""simple docstring"""


			return fibonacci_digits_index(lowerCAmelCase__  )


if __name__ == "__main__":
	print(solution(int(str(input()).strip())))






